Talent Management
Looking at data on 870 different employees for attrition and salary trends/predictions.
Kendall Scott
4/16/2022
##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union## Loading required package: viridisLitelibrary(stringr)
casestudy2 = read.csv(".//CaseStudy2-data.csv") #casestudy2-data.csv
#Attrition by Department
ggplot(casestudy2, aes(x=as.factor(Department), fill=Attrition))+
geom_bar(aes( y=..count../tapply(..count.., ..x.. ,sum)[..x..]), position="stack" , width=0.5) +
geom_text(aes( y=..count../tapply(..count.., ..x.. ,sum)[..x..], label=scales::percent(..count../tapply(..count.., ..x.. ,sum)[..x..]) ),
stat="count", position=position_stack(0.9), vjust=0.5)+
xlab('Department') +
ylab('Percent of Attrition')+
scale_x_discrete(labels = function(x) str_wrap(x, width = 10))+
theme(axis.text = element_text(size = 7))
casestudy2$Attritioncalc=case_when(
casestudy2$Attrition =='Yes' ~ 1,
TRUE ~ 0
)
#summary
er<-casestudy2 %>% group_by(Department) %>% summarize(meanincome = mean(MonthlyIncome), calcAttrition = (sum(Attritioncalc)/n()), Employees = n()) %>% arrange(desc(Employees))
er ## # A tibble: 3 × 4
## Department meanincome calcAttrition Employees
## <chr> <dbl> <dbl> <int>
## 1 Research & Development 6173. 0.133 562
## 2 Sales 6789. 0.216 273
## 3 Human Resources 6776. 0.171 35ggplot() +
geom_bar( data=er, aes(x=Department, color=Employees, size=calcAttrition, alpha=0.5)) +
scale_size(range = c(1, 10), name="Attrition %")+
scale_color_viridis(option="viridis", name="Employees" ) +
ggtitle("Attrition by Department")
#Attrition % by Job Role and Department
#summary table
er<-casestudy2 %>% group_by(Department, JobRole) %>% summarize(meanincome = mean(MonthlyIncome), calcAttrition = (sum(Attritioncalc)/n()), Employees = n()) %>% arrange(desc(Employees))## `summarise()` has grouped output by 'Department'. You can override using the `.groups` argument.## # A tibble: 11 × 5
## # Groups: Department [3]
## Department JobRole meanincome calcAttrition Employees
## <chr> <chr> <dbl> <dbl> <int>
## 1 Sales Sales Executive 6892. 0.165 200
## 2 Research & Development Research Scientist 3259. 0.186 172
## 3 Research & Development Laboratory Technic… 3222. 0.196 153
## 4 Research & Development Manufacturing Dire… 7505. 0.0230 87
## 5 Research & Development Healthcare Represe… 7435. 0.105 76
## 6 Sales Sales Representati… 2653. 0.453 53
## 7 Research & Development Research Director 15750. 0.0196 51
## 8 Human Resources Human Resources 3285. 0.222 27
## 9 Research & Development Manager 17139. 0.0870 23
## 10 Sales Manager 16719. 0.1 20
## 11 Human Resources Manager 18560 0 8#graph
ggplot() +
geom_polygon(data = er, aes(x=Department, y = JobRole),color = "white", fill="grey", alpha=0.5) +
geom_point( data=er, aes(x=Department, y=JobRole, color=Employees, size=calcAttrition, alpha=0.5)) +
scale_color_viridis(option="viridis", name="Employees" ) +
scale_size(range = c(1, 10), name="Attrition %")+
ggtitle("Attrition by Role")
#Attrition % by Job Level and Department
#summary
er<-casestudy2 %>% group_by(Department, JobLevel) %>% summarize(meanincome = mean(MonthlyIncome), calcAttrition = (sum(Attritioncalc)/n()), Employees = n()) %>% arrange(desc(Employees))## `summarise()` has grouped output by 'Department'. You can override using the `.groups` argument.## # A tibble: 15 × 5
## # Groups: Department [3]
## Department JobLevel meanincome calcAttrition Employees
## <chr> <int> <dbl> <dbl> <int>
## 1 Research & Development 1 2793. 0.219 256
## 2 Research & Development 2 5435. 0.0542 166
## 3 Sales 2 5678. 0.146 144
## 4 Research & Development 3 10248. 0.0909 77
## 5 Sales 3 9331. 0.189 53
## 6 Sales 1 2519. 0.48 50
## 7 Research & Development 4 15374. 0.0256 39
## 8 Research & Development 5 19304. 0.0833 24
## 9 Human Resources 1 2691. 0.261 23
## 10 Sales 4 14863. 0.105 19
## 11 Sales 5 18965. 0.286 7
## 12 Human Resources 5 19207. 0 6
## 13 Human Resources 2 4982. 0 2
## 14 Human Resources 3 8412. 0 2
## 15 Human Resources 4 16618 0 2#graph
ggplot() +
geom_point( data=er, aes(x=Department, y=JobLevel, color=Employees, size=calcAttrition, alpha=0.5)) +
scale_color_viridis(option="viridis", name="Employees" ) +
scale_size(range = c(1, 10), name="Attrition %")+
ggtitle("Attrition by Job Level")
Including Plots
You can also embed plots, for example:
# k-NN or naive Bayes but may also use other models (logistic regression, random forest, LDA, SVM, etc)
#as long as you compare the results between the two or more models.
#You may then use any of the models to fulfill the 60/60 sensitivity/specificity requirement.
#This goes for regression as well; you must use linear regression but may include additional models for comparison and use in the competition (LASSO, random forest, ensemble models, etc.).
casestudy2.noattrition = read.csv(".//CaseStudy2CompSet No Salary.csv")
library(class)
library(caret)## Loading required package: latticelibrary(e1071)
clean_casestudy2 = data.frame(
Attrition=casestudy2$Attrition,
Age = scale(casestudy2$Age),
JobInvolvement=scale(casestudy2$JobInvolvement),
JobLevel=scale(casestudy2$JobLevel),
Distance=scale(casestudy2$DistanceFromHome),
StockOptionLevel=scale(casestudy2$StockOptionLevel),
EnvironmentSatisfaction=scale(casestudy2$EnvironmentSatisfaction),
RelationshipSatisfaction=scale(casestudy2$RelationshipSatisfaction),
JobSatisfaction=scale(casestudy2$JobSatisfaction),
YearSinceLastPromotion=scale(casestudy2$YearsSinceLastPromotion),
YearsinCurrentRole=scale(casestudy2$YearsInCurrentRole),
Education=scale(casestudy2$Education)
)
casestudy2.noatt= data.frame(
Attrition=casestudy2.noattrition$Attrition,
Age = scale(casestudy2.noattrition$Age),
JobInvolvement=scale(casestudy2.noattrition$JobInvolvement),
JobLevel=scale(casestudy2.noattrition$JobLevel),
Distance=scale(casestudy2.noattrition$DistanceFromHome),
StockOptionLevel=scale(casestudy2.noattrition$StockOptionLevel),
EnvironmentSatisfaction=scale(casestudy2.noattrition$EnvironmentSatisfaction),
RelationshipSatisfaction=scale(casestudy2.noattrition$RelationshipSatisfaction),
JobSatisfaction=scale(casestudy2.noattrition$JobSatisfaction),
YearSinceLastPromotion=scale(casestudy2.noattrition$YearsSinceLastPromotion),
YearsinCurrentRole=scale(casestudy2.noattrition$YearsInCurrentRole),
Education=scale(casestudy2.noattrition$Education))
test= casestudy2.noatt
train=clean_casestudy2
#confusion matrix results for each row
numks = 30
masterAcc = matrix(nrow = numks)
masterSens = matrix(nrow = numks)
masterSpec = matrix(nrow = numks)
masterK = matrix(nrow = numks)
data(attrition)## Warning in data(attrition): data set 'attrition' not foundset.seed(1)
i=1
for(i in 1:numks)
{
classifications = knn(train[,c(2:12)],test[,c(2:12)],as.factor(train$Attrition), prob = TRUE, k = i)
#results for accuracty, sensitivity, and specificity
table(as.factor(test$Attrition),classifications)
CM = confusionMatrix(table(as.factor(test$Attrition),classifications))
masterAcc[i] = CM$overall[1]
masterSens[i]=CM[["byClass"]][["Sensitivity"]][1]
masterSpec[i]=CM[["byClass"]][["Specificity"]][1]
masterK[i]=i
}
Overall=cbind(masterAcc,masterSens, masterSpec, masterK)
Overall=as.data.frame(Overall)
#renaming CM column results
Overall=rename(Overall, Accuracy=V1, Sensitivity=V2, Specificity=V3,K=V4)
MeanAcc = colMeans(Overall)
MeanAcc## Accuracy Sensitivity Specificity K
## 0.8191111 0.8356097 0.3526761 15.5000000## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──## ✓ tibble 3.1.6 ✓ purrr 0.3.4
## ✓ tidyr 1.1.4 ✓ forcats 0.5.1
## ✓ readr 2.1.1## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
## x purrr::lift() masks caret::lift()df <- Overall %>%
select(K, Accuracy, Sensitivity, Specificity) %>%
gather(key = "variable", value = "value", -K)
#graph of knn results
ggplot(df, aes(x = K, y = value)) +
geom_line(aes(color = variable, linetype = variable)) +
scale_color_manual(values = c("black", "steelblue", "blue"))+ geom_vline(xintercept=16,color="darkgreen")+ ylab('Confusion Matrix Measurments')+ggtitle("KNN Results")
#based on results-- k=16 is the best selection
casestudy2.noattrition = read.csv(".//CaseStudy2CompSet No Attrition.csv")
casestudy2.noattrition$Attrition= classifications = knn(train[,c(2:12)],test[,c(2:12)],as.factor(train$Attrition), prob = TRUE, k = 16)Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.
##
## Call:
## lm(formula = MonthlyIncome ~ JobLevel + YearsinCurrentRole, data = clean_casestudy2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4981.4 -928.0 71.8 693.6 3751.1
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6390.26 47.91 133.382 <2e-16 ***
## JobLevel 4397.74 52.09 84.425 <2e-16 ***
## YearsinCurrentRole -57.23 52.09 -1.099 0.272
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1413 on 867 degrees of freedom
## Multiple R-squared: 0.9057, Adjusted R-squared: 0.9055
## F-statistic: 4166 on 2 and 867 DF, p-value: < 2.2e-16## 1 2 3 4 5 6 7 8
## 6267.271 18291.289 10301.484 10175.690 2217.334 10222.863 2233.058 6298.720
## 9 10 11 12 13 14 15 16
## 2248.783 6267.271 18212.667 2233.058 10254.311 2264.507 2170.161 6172.925
## 17 18 19 20 21 22 23 24
## 6188.650 6298.720 2264.507 6298.720 6267.271 6267.271 6188.650 6267.271
## 25 26 27 28 29 30 31 32
## 6298.720 18259.840 6172.925 6267.271 10301.484 2217.334 6188.650 2091.539
## 33 34 35 36 37 38 39 40
## 14257.076 2264.507 6298.720 2248.783 10144.241 6267.271 18354.186 2233.058
## 41 42 43 44 45 46 47 48
## 14225.627 6188.650 2233.058 18401.359 2264.507 2122.988 6298.720 10222.863
## 49 50 51 52 53 54 55 56
## 2264.507 2233.058 2264.507 18275.564 2185.885 6188.650 6188.650 2217.334
## 57 58 59 60 61 62 63 64
## 2233.058 2122.988 10301.484 2264.507 10222.863 10222.863 2233.058 2264.507
## 65 66 67 68 69 70 71 72
## 2264.507 2233.058 6125.752 2233.058 2233.058 2075.815 2217.334 6235.823
## 73 74 75 76 77 78 79 80
## 6298.720 2264.507 10222.863 6172.925 10191.414 14367.146 10222.863 2264.507
## 81 82 83 84 85 86 87 88
## 6298.720 6188.650 6251.547 6188.650 6078.579 14319.973 6188.650 2233.058
## 89 90 91 92 93 94 95 96
## 10207.138 18369.910 2264.507 10207.138 2264.507 6267.271 18338.462 6047.131
## 97 98 99 100 101 102 103 104
## 14178.454 6298.720 2233.058 2201.610 2264.507 10222.863 6267.271 2233.058
## 105 106 107 108 109 110 111 112
## 14241.351 2154.437 6188.650 18259.840 6188.650 2233.058 2248.783 14241.351
## 113 114 115 116 117 118 119 120
## 10238.587 6298.720 6188.650 10332.933 6251.547 6251.547 2138.712 6125.752
## 121 122 123 124 125 126 127 128
## 10222.863 2233.058 10270.036 6188.650 6188.650 6172.925 2233.058 6172.925
## 129 130 131 132 133 134 135 136
## 6188.650 14099.832 6298.720 6282.996 6220.098 6235.823 6188.650 6188.650
## 137 138 139 140 141 142 143 144
## 2264.507 10222.863 2233.058 10285.760 14115.557 2233.058 10222.863 6235.823
## 145 146 147 148 149 150 151 152
## 6125.752 6251.547 2154.437 6267.271 2217.334 6251.547 6282.996 6188.650
## 153 154 155 156 157 158 159 160
## 6172.925 6267.271 2264.507 6267.271 6172.925 14257.076 10222.863 10332.933
## 161 162 163 164 165 166 167 168
## 6172.925 10222.863 6298.720 6267.271 10191.414 2233.058 14335.697 14351.422
## 169 170 171 172 173 174 175 176
## 6157.201 2185.885 6188.650 6188.650 10301.484 10207.138 14162.730 2233.058
## 177 178 179 180 181 182 183 184
## 18369.910 2233.058 10207.138 6251.547 10222.863 6188.650 2233.058 10332.933
## 185 186 187 188 189 190 191 192
## 2217.334 2233.058 6188.650 14335.697 18338.462 6235.823 6172.925 10301.484
## 193 194 195 196 197 198 199 200
## 6188.650 6188.650 2217.334 2264.507 2233.058 14241.351 10238.587 2264.507
## 201 202 203 204 205 206 207 208
## 14367.146 6235.823 6094.304 2154.437 2217.334 2154.437 6251.547 6298.720
## 209 210 211 212 213 214 215 216
## 2233.058 10222.863 14257.076 18401.359 2264.507 6251.547 2233.058 2248.783
## 217 218 219 220 221 222 223 224
## 6298.720 14099.832 2201.610 2185.885 6220.098 6282.996 6220.098 2264.507
## 225 226 227 228 229 230 231 232
## 2233.058 2138.712 2264.507 2233.058 6267.271 14194.178 6188.650 2233.058
## 233 234 235 236 237 238 239 240
## 14288.524 6267.271 14178.454 10317.209 18307.013 6235.823 2264.507 10159.965
## 241 242 243 244 245 246 247 248
## 10191.414 18401.359 2201.610 2233.058 6188.650 2217.334 10191.414 2233.058
## 249 250 251 252 253 254 255 256
## 6267.271 14115.557 2248.783 6188.650 2264.507 2264.507 10207.138 2233.058
## 257 258 259 260 261 262 263 264
## 2233.058 6267.271 10285.760 2233.058 2264.507 2154.437 2201.610 2248.783
## 265 266 267 268 269 270 271 272
## 2217.334 2201.610 14241.351 2264.507 6188.650 6267.271 6188.650 6172.925
## 273 274 275 276 277 278 279 280
## 6172.925 6267.271 18165.494 18369.910 2248.783 2154.437 6235.823 6267.271
## 281 282 283 284 285 286 287 288
## 6157.201 2060.091 6251.547 6188.650 2170.161 6078.579 2264.507 2233.058
## 289 290 291 292 293 294 295 296
## 10222.863 2233.058 2217.334 6172.925 2154.437 10207.138 14257.076 2233.058
## 297 298 299 300 301 302 303 304
## 6157.201 6188.650 2233.058 2233.058 6172.925 10207.138 2233.058 6188.650
## 305 306 307 308 309 310 311 312
## 2264.507 2233.058 2264.507 2264.507 6188.650 6267.271 6267.271 14272.800
## 313 314 315 316 317 318 319 320
## 2233.058 6298.720 6267.271 6298.720 2217.334 6251.547 6235.823 2264.507
## 321 322 323 324 325 326 327 328
## 2264.507 2122.988 2201.610 6094.304 14272.800 2248.783 18291.289 6251.547
## 329 330 331 332 333 334 335 336
## 6125.752 14257.076 14257.076 10207.138 2217.334 10222.863 18369.910 6188.650
## 337 338 339 340 341 342 343 344
## 6267.271 6267.271 6298.720 2264.507 2248.783 2233.058 10301.484 2264.507
## 345 346 347 348 349 350 351 352
## 6251.547 14241.351 6267.271 6220.098 2264.507 14257.076 10222.863 6188.650
## 353 354 355 356 357 358 359 360
## 6282.996 6267.271 10112.792 6157.201 2201.610 2233.058 6172.925 2060.091
## 361 362 363 364 365 366 367 368
## 18118.321 2264.507 18181.218 18291.289 18259.840 14257.076 6172.925 6235.823
## 369 370 371 372 373 374 375 376
## 2233.058 6188.650 6251.547 10207.138 10191.414 2201.610 2233.058 14272.800
## 377 378 379 380 381 382 383 384
## 2060.091 6298.720 2201.610 2233.058 18369.910 2154.437 6267.271 2264.507
## 385 386 387 388 389 390 391 392
## 2264.507 10144.241 10222.863 6267.271 6188.650 2233.058 6267.271 6267.271
## 393 394 395 396 397 398 399 400
## 2233.058 10128.517 2248.783 2233.058 14335.697 2233.058 6110.028 10301.484
## 401 402 403 404 405 406 407 408
## 6235.823 6251.547 2233.058 10301.484 2185.885 14367.146 6188.650 2154.437
## 409 410 411 412 413 414 415 416
## 14241.351 14351.422 2201.610 10207.138 2264.507 6298.720 2233.058 6188.650
## 417 418 419 420 421 422 423 424
## 2154.437 2264.507 6172.925 10301.484 6267.271 6125.752 6188.650 2264.507
## 425 426 427 428 429 430 431 432
## 6251.547 10332.933 2264.507 2233.058 2233.058 6157.201 6235.823 2233.058
## 433 434 435 436 437 438 439 440
## 6251.547 2264.507 10332.933 6188.650 2233.058 2154.437 2233.058 6188.650
## 441 442 443 444 445 446 447 448
## 6188.650 2217.334 2264.507 2233.058 18385.635 2185.885 6125.752 6298.720
## 449 450 451 452 453 454 455 456
## 6251.547 6188.650 6188.650 2233.058 6188.650 6235.823 14335.697 18275.564
## 457 458 459 460 461 462 463 464
## 6298.720 6188.650 2233.058 6235.823 2233.058 2217.334 10301.484 6235.823
## 465 466 467 468 469 470 471 472
## 2264.507 2233.058 2233.058 2264.507 6267.271 6267.271 2217.334 2264.507
## 473 474 475 476 477 478 479 480
## 10332.933 2185.885 2233.058 6204.374 6298.720 6298.720 2201.610 10159.965
## 481 482 483 484 485 486 487 488
## 6251.547 2201.610 6141.477 2233.058 10175.690 6235.823 2264.507 2233.058
## 489 490 491 492 493 494 495 496
## 10332.933 6282.996 18369.910 14335.697 6267.271 10207.138 6157.201 14272.800
## 497 498 499 500 501 502 503 504
## 2233.058 2233.058 6188.650 6267.271 10191.414 2233.058 2154.437 10191.414
## 505 506 507 508 509 510 511 512
## 14099.832 6282.996 2264.507 10301.484 2138.712 18165.494 6188.650 10270.036
## 513 514 515 516 517 518 519 520
## 2233.058 10112.792 2233.058 6282.996 2264.507 2217.334 6267.271 2233.058
## 521 522 523 524 525 526 527 528
## 2233.058 2248.783 10332.933 18259.840 2264.507 10222.863 6235.823 6220.098
## 529 530 531 532 533 534 535 536
## 2201.610 2201.610 10191.414 6298.720 2154.437 6282.996 2154.437 2201.610
## 537 538 539 540 541 542 543 544
## 10332.933 2233.058 2217.334 6235.823 6188.650 2138.712 2248.783 6298.720
## 545 546 547 548 549 550 551 552
## 2185.885 2122.988 10191.414 6298.720 10207.138 2217.334 18291.289 2248.783
## 553 554 555 556 557 558 559 560
## 6282.996 2264.507 6267.271 6298.720 6298.720 2185.885 6172.925 6188.650
## 561 562 563 564 565 566 567 568
## 2154.437 14335.697 2233.058 10222.863 2233.058 2217.334 6267.271 6172.925
## 569 570 571 572 573 574 575 576
## 6235.823 14194.178 6235.823 2264.507 6188.650 6172.925 6251.547 6220.098
## 577 578 579 580 581 582 583 584
## 2264.507 2233.058 6267.271 6298.720 2233.058 14367.146 6298.720 6188.650
## 585 586 587 588 589 590 591 592
## 6267.271 6267.271 6125.752 2217.334 2233.058 2233.058 2233.058 6157.201
## 593 594 595 596 597 598 599 600
## 2233.058 2170.161 10097.068 6172.925 10175.690 6298.720 6267.271 2264.507
## 601 602 603 604 605 606 607 608
## 2264.507 6220.098 10222.863 2138.712 2233.058 14351.422 6251.547 6298.720
## 609 610 611 612 613 614 615 616
## 2154.437 2233.058 6188.650 6188.650 2154.437 2264.507 10270.036 6141.477
## 617 618 619 620 621 622 623 624
## 2201.610 6251.547 10222.863 2264.507 2264.507 6141.477 2233.058 10332.933
## 625 626 627 628 629 630 631 632
## 2264.507 2217.334 6235.823 10175.690 10301.484 10222.863 2233.058 10207.138
## 633 634 635 636 637 638 639 640
## 6298.720 2264.507 10332.933 2233.058 14272.800 10222.863 2185.885 2217.334
## 641 642 643 644 645 646 647 648
## 2264.507 6235.823 14257.076 2233.058 2264.507 10159.965 2264.507 2233.058
## 649 650 651 652 653 654 655 656
## 6094.304 2233.058 2264.507 6188.650 10222.863 10332.933 2233.058 6251.547
## 657 658 659 660 661 662 663 664
## 10191.414 14225.627 6235.823 6220.098 6172.925 2217.334 10238.587 2233.058
## 665 666 667 668 669 670 671 672
## 6220.098 6267.271 10175.690 2217.334 6235.823 14225.627 10301.484 6267.271
## 673 674 675 676 677 678 679 680
## 6267.271 2233.058 2233.058 18338.462 6188.650 6157.201 6267.271 2154.437
## 681 682 683 684 685 686 687 688
## 6267.271 2233.058 6267.271 10285.760 2233.058 10207.138 10222.863 2233.058
## 689 690 691 692 693 694 695 696
## 2264.507 2107.264 6251.547 10222.863 6298.720 2264.507 6251.547 2233.058
## 697 698 699 700 701 702 703 704
## 14335.697 6125.752 2264.507 18369.910 2264.507 6220.098 10301.484 2248.783
## 705 706 707 708 709 710 711 712
## 2233.058 6251.547 6282.996 2233.058 2201.610 14272.800 10317.209 14367.146
## 713 714 715 716 717 718 719 720
## 2264.507 10191.414 10222.863 2264.507 6251.547 6188.650 6157.201 10222.863
## 721 722 723 724 725 726 727 728
## 10222.863 6251.547 2217.334 10207.138 2264.507 6267.271 2201.610 2217.334
## 729 730 731 732 733 734 735 736
## 2264.507 2248.783 6298.720 6298.720 2264.507 6267.271 2264.507 6188.650
## 737 738 739 740 741 742 743 744
## 14225.627 6282.996 6267.271 6267.271 6267.271 10270.036 6172.925 2233.058
## 745 746 747 748 749 750 751 752
## 6251.547 2248.783 2233.058 6157.201 2233.058 2248.783 2233.058 2233.058
## 753 754 755 756 757 758 759 760
## 14241.351 10222.863 10285.760 2233.058 6204.374 6267.271 2264.507 18244.116
## 761 762 763 764 765 766 767 768
## 2264.507 14147.005 6267.271 6188.650 2233.058 6188.650 2170.161 10332.933
## 769 770 771 772 773 774 775 776
## 6251.547 6141.477 6188.650 10128.517 10238.587 2264.507 10207.138 2233.058
## 777 778 779 780 781 782 783 784
## 2217.334 2264.507 2154.437 2233.058 2217.334 6188.650 2217.334 6188.650
## 785 786 787 788 789 790 791 792
## 6298.720 14335.697 10301.484 6267.271 18401.359 2233.058 6267.271 2233.058
## 793 794 795 796 797 798 799 800
## 10270.036 10112.792 6188.650 2138.712 14257.076 2264.507 2248.783 6235.823
## 801 802 803 804 805 806 807 808
## 6251.547 6220.098 14225.627 10222.863 6267.271 10207.138 6267.271 2233.058
## 809 810 811 812 813 814 815 816
## 2264.507 2233.058 6157.201 2233.058 6267.271 18369.910 2233.058 2107.264
## 817 818 819 820 821 822 823 824
## 6298.720 10191.414 2217.334 6298.720 2233.058 6267.271 6188.650 2233.058
## 825 826 827 828 829 830 831 832
## 6267.271 18369.910 10332.933 6267.271 2248.783 18322.737 6172.925 2264.507
## 833 834 835 836 837 838 839 840
## 2185.885 2248.783 14225.627 10254.311 2201.610 6188.650 10159.965 18244.116
## 841 842 843 844 845 846 847 848
## 2264.507 10191.414 14241.351 10222.863 6267.271 6298.720 2264.507 10175.690
## 849 850 851 852 853 854 855 856
## 6235.823 2154.437 6267.271 2264.507 6220.098 6251.547 6267.271 2264.507
## 857 858 859 860 861 862 863 864
## 2233.058 10191.414 2264.507 10222.863 2264.507 10222.863 6267.271 14335.697
## 865 866 867 868 869 870
## 10317.209 10191.414 6267.271 6235.823 2233.058 6267.271
##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layout##
## Attaching package: 'reshape2'## The following object is masked from 'package:tidyr':
##
## smiths##
## Call:
## lm(formula = MonthlyIncome ~ JobLevel + YearsinCurrentRole, data = clean_casestudy2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4981.4 -928.0 71.8 693.6 3751.1
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6390.26 47.91 133.382 <2e-16 ***
## JobLevel 4397.74 52.09 84.425 <2e-16 ***
## YearsinCurrentRole -57.23 52.09 -1.099 0.272
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1413 on 867 degrees of freedom
## Multiple R-squared: 0.9057, Adjusted R-squared: 0.9055
## F-statistic: 4166 on 2 and 867 DF, p-value: < 2.2e-16## Warning: 'surface' objects don't have these attributes: 'mode'
## Valid attributes include:
## '_deprecated', 'autocolorscale', 'cauto', 'cmax', 'cmid', 'cmin', 'coloraxis', 'colorbar', 'colorscale', 'connectgaps', 'contours', 'customdata', 'customdatasrc', 'hidesurface', 'hoverinfo', 'hoverinfosrc', 'hoverlabel', 'hovertemplate', 'hovertemplatesrc', 'hovertext', 'hovertextsrc', 'ids', 'idssrc', 'legendgroup', 'legendgrouptitle', 'legendrank', 'lighting', 'lightposition', 'meta', 'metasrc', 'name', 'opacity', 'opacityscale', 'reversescale', 'scene', 'showlegend', 'showscale', 'stream', 'surfacecolor', 'surfacecolorsrc', 'text', 'textsrc', 'type', 'uid', 'uirevision', 'visible', 'x', 'xcalendar', 'xhoverformat', 'xsrc', 'y', 'ycalendar', 'yhoverformat', 'ysrc', 'z', 'zcalendar', 'zhoverformat', 'zsrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule', '_bbox'